The MapReduce distributed programming framework has become popular, despite evidence that current implementations are inefficient, requiring far more hardware than a traditional r...
Eaman Jahani, Michael J. Cafarella, Christopher R&...
Timely and cost-effective processing of large datasets has become a critical ingredient for the success of many academic, government, and industrial organizations. The combination...
Abstract—Dynamic runtimes can simplify parallel programming by automatically managing concurrency and locality without further burdening the programmer. Nevertheless, implementin...
Richard M. Yoo, Anthony Romano, Christos Kozyrakis
In many scientific domains, researchers are turning to large-scale behavioral simulations to better understand real-world phenomena. While there has been a great deal of work on s...
Guozhang Wang, Marcos Antonio Vaz Salles, Benjamin...
We present a system for allocating resources in shared data and compute clusters that improves MapReduce job scheduling in three ways. First, the system uses regulated and user-as...